Intelligent robots must be able to not only adapt an existing behavior on the fly in the
face of environmental perturbation, but must also be able to generate new, compensating
behavior after severe, unanticipated change such as body damage. In this talk I will
describe a physical robot with this latter capability, a capability we refer to as resiliency.
The robot achieves this by (1) creating an approximate simulation of itself;
(2) optimizing a controller using this simulator; (3) using the controller in reality;
(4) experiencing body damage; (5) indirectly inferring the damage and updating the simulator;
(6) re-optimizing a new controller in the altered simulator; and (7) executing this compensatory
controller in reality. I will also describe recent work generalizing this approach to robot teams.